Overview

Dataset statistics

Number of variables46
Number of observations46927
Missing cells243504
Missing cells (%)11.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.0 MiB
Average record size in memory313.0 B

Variable types

Numeric22
DateTime2
Categorical13
Text4
Boolean5

Alerts

booking_datetime is highly overall correlated with checkin_date and 2 other fieldsHigh correlation
checkin_date is highly overall correlated with booking_datetime and 1 other fieldsHigh correlation
checkout_date is highly overall correlated with booking_datetime and 1 other fieldsHigh correlation
hotel_star_rating is highly overall correlated with price_per_guest_per_nightHigh correlation
no_of_adults is highly overall correlated with no_of_room and 1 other fieldsHigh correlation
no_of_room is highly overall correlated with no_of_adultsHigh correlation
language is highly overall correlated with original_payment_currencyHigh correlation
original_selling_amount is highly overall correlated with amount_nights and 1 other fieldsHigh correlation
distance_booking_checkin is highly overall correlated with booking_datetimeHigh correlation
amount_guests is highly overall correlated with no_of_adultsHigh correlation
amount_nights is highly overall correlated with original_selling_amountHigh correlation
price_per_guest_per_night is highly overall correlated with hotel_star_rating and 1 other fieldsHigh correlation
charge_option is highly overall correlated with pay_nowHigh correlation
original_payment_currency is highly overall correlated with languageHigh correlation
pay_now is highly overall correlated with charge_optionHigh correlation
no_of_extra_bed is highly imbalanced (96.1%)Imbalance
original_payment_method is highly imbalanced (58.3%)Imbalance
original_payment_type is highly imbalanced (91.4%)Imbalance
request_latecheckin is highly imbalanced (82.6%)Imbalance
request_airport is highly imbalanced (93.7%)Imbalance
request_earlycheckin is highly imbalanced (78.8%)Imbalance
request_nonesmoke has 20030 (42.7%) missing valuesMissing
request_latecheckin has 20030 (42.7%) missing valuesMissing
request_highfloor has 20030 (42.7%) missing valuesMissing
request_largebed has 20030 (42.7%) missing valuesMissing
request_twinbeds has 20030 (42.7%) missing valuesMissing
request_airport has 20030 (42.7%) missing valuesMissing
request_earlycheckin has 20030 (42.7%) missing valuesMissing
hotel_brand_code has 34699 (73.9%) missing valuesMissing
hotel_chain_code has 34343 (73.2%) missing valuesMissing
cancellation_datetime has 34250 (73.0%) missing valuesMissing
original_selling_amount is highly skewed (γ1 = 36.55009602)Skewed
h_booking_id has unique valuesUnique
hotel_star_rating has 2060 (4.4%) zerosZeros
accommadation_type_name has 938 (2.0%) zerosZeros
no_of_children has 42690 (91.0%) zerosZeros
distance_booking_checkin has 11952 (25.5%) zerosZeros

Reproduction

Analysis started2023-06-08 12:13:06.359262
Analysis finished2023-06-08 12:14:47.076128
Duration1 minute and 40.72 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

h_booking_id
Real number (ℝ)

Distinct46927
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.7482761 × 1016
Minimum-9.2231941 × 1018
Maximum9.2233383 × 1018
Zeros0
Zeros (%)0.0%
Negative23545
Negative (%)50.2%
Memory size366.7 KiB
2023-06-08T15:14:47.279121image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum-9.2231941 × 1018
5-th percentile-8.3231332 × 1018
Q1-4.6146133 × 1018
median-3.2064787 × 1016
Q34.5760991 × 1018
95-th percentile8.2846133 × 1018
Maximum9.2233383 × 1018
Range-2.1169456 × 1014
Interquartile range (IQR)9.1907124 × 1018

Descriptive statistics

Standard deviation5.3247643 × 1018
Coefficient of variation (CV)-304.57227
Kurtosis-1.1991323
Mean-1.7482761 × 1016
Median Absolute Deviation (MAD)4.5955943 × 1018
Skewness0.0033792406
Sum-8.756789 × 1018
Variance2.8353114 × 1037
MonotonicityNot monotonic
2023-06-08T15:14:47.593987image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.861445259 × 10181
 
< 0.1%
-5.859385366 × 10181
 
< 0.1%
4.102580392 × 10181
 
< 0.1%
1.795941529 × 10181
 
< 0.1%
-5.721949141 × 10181
 
< 0.1%
-4.284526584 × 10181
 
< 0.1%
2.103613272 × 10181
 
< 0.1%
5.236811047 × 10181
 
< 0.1%
-2.3832694 × 10181
 
< 0.1%
-2.788888768 × 10181
 
< 0.1%
Other values (46917) 46917
> 99.9%
ValueCountFrequency (%)
-9.223194056 × 10181
< 0.1%
-9.222713784 × 10181
< 0.1%
-9.222411208 × 10181
< 0.1%
-9.222220846 × 10181
< 0.1%
-9.220467519 × 10181
< 0.1%
-9.219746746 × 10181
< 0.1%
-9.219730436 × 10181
< 0.1%
-9.219588531 × 10181
< 0.1%
-9.219312947 × 10181
< 0.1%
-9.219139934 × 10181
< 0.1%
ValueCountFrequency (%)
9.223338324 × 10181
< 0.1%
9.223221736 × 10181
< 0.1%
9.222651807 × 10181
< 0.1%
9.222015612 × 10181
< 0.1%
9.221958225 × 10181
< 0.1%
9.221798878 × 10181
< 0.1%
9.221611986 × 10181
< 0.1%
9.221457559 × 10181
< 0.1%
9.220807687 × 10181
< 0.1%
9.220787994 × 10181
< 0.1%

booking_datetime
Real number (ℝ)

Distinct357
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199.2134
Minimum1
Maximum365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-08T15:14:47.791820image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile95
Q1180
median206
Q3234
95-th percentile262
Maximum365
Range364
Interquartile range (IQR)54

Descriptive statistics

Standard deviation49.987481
Coefficient of variation (CV)0.2509243
Kurtosis1.8416776
Mean199.2134
Median Absolute Deviation (MAD)27
Skewness-1.0796464
Sum9348487
Variance2498.7483
MonotonicityNot monotonic
2023-06-08T15:14:47.954519image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
183 541
 
1.2%
184 526
 
1.1%
186 505
 
1.1%
205 505
 
1.1%
199 500
 
1.1%
185 497
 
1.1%
213 491
 
1.0%
187 491
 
1.0%
193 490
 
1.0%
197 488
 
1.0%
Other values (347) 41893
89.3%
ValueCountFrequency (%)
1 8
< 0.1%
2 10
< 0.1%
3 9
< 0.1%
4 3
 
< 0.1%
5 9
< 0.1%
6 5
 
< 0.1%
7 10
< 0.1%
8 16
< 0.1%
9 17
< 0.1%
10 12
< 0.1%
ValueCountFrequency (%)
365 11
< 0.1%
364 3
 
< 0.1%
363 4
 
< 0.1%
362 5
< 0.1%
361 8
< 0.1%
360 5
< 0.1%
359 1
 
< 0.1%
358 3
 
< 0.1%
357 7
< 0.1%
356 2
 
< 0.1%

checkin_date
Real number (ℝ)

Distinct105
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean226.65112
Minimum158
Maximum272
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-08T15:14:48.117867image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum158
5-th percentile186
Q1205
median226
Q3250
95-th percentile267
Maximum272
Range114
Interquartile range (IQR)45

Descriptive statistics

Standard deviation26.196808
Coefficient of variation (CV)0.11558208
Kurtosis-1.1697685
Mean226.65112
Median Absolute Deviation (MAD)23
Skewness0.0039606809
Sum10636057
Variance686.27273
MonotonicityNot monotonic
2023-06-08T15:14:48.280260image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
209 864
 
1.8%
258 801
 
1.7%
208 784
 
1.7%
230 784
 
1.7%
265 771
 
1.6%
251 748
 
1.6%
188 705
 
1.5%
223 691
 
1.5%
216 669
 
1.4%
195 666
 
1.4%
Other values (95) 39444
84.1%
ValueCountFrequency (%)
158 1
 
< 0.1%
166 1
 
< 0.1%
167 1
 
< 0.1%
168 1
 
< 0.1%
172 5
 
< 0.1%
173 3
 
< 0.1%
174 7
< 0.1%
175 13
< 0.1%
176 14
< 0.1%
177 15
< 0.1%
ValueCountFrequency (%)
272 482
1.0%
271 515
1.1%
270 406
0.9%
269 419
0.9%
268 427
0.9%
267 436
0.9%
266 547
1.2%
265 771
1.6%
264 647
1.4%
263 495
1.1%

checkout_date
Real number (ℝ)

Distinct91
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean228.64479
Minimum183
Maximum273
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-08T15:14:48.445986image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum183
5-th percentile188
Q1207
median229
Q3252
95-th percentile269
Maximum273
Range90
Interquartile range (IQR)45

Descriptive statistics

Standard deviation26.114705
Coefficient of variation (CV)0.11421518
Kurtosis-1.1822042
Mean228.64479
Median Absolute Deviation (MAD)23
Skewness0.0061513478
Sum10729614
Variance681.97779
MonotonicityNot monotonic
2023-06-08T15:14:48.602985image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
210 999
 
2.1%
231 868
 
1.8%
259 843
 
1.8%
273 792
 
1.7%
189 784
 
1.7%
196 781
 
1.7%
266 774
 
1.6%
217 773
 
1.6%
252 763
 
1.6%
238 732
 
1.6%
Other values (81) 38818
82.7%
ValueCountFrequency (%)
183 504
1.1%
184 409
0.9%
185 413
0.9%
186 403
0.9%
187 424
0.9%
188 484
1.0%
189 784
1.7%
190 423
0.9%
191 402
0.9%
192 383
0.8%
ValueCountFrequency (%)
273 792
1.7%
272 554
1.2%
271 444
0.9%
270 457
1.0%
269 454
1.0%
268 466
1.0%
267 661
1.4%
266 774
1.6%
265 552
1.2%
264 420
0.9%

hotel_id
Real number (ℝ)

Distinct24969
Distinct (%)53.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1341712.9
Minimum1
Maximum5823993
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-08T15:14:48.767454image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10917.6
Q1255829
median798535
Q32284662
95-th percentile4168202
Maximum5823993
Range5823992
Interquartile range (IQR)2028833

Descriptive statistics

Standard deviation1361519.7
Coefficient of variation (CV)1.0147624
Kurtosis0.077266214
Mean1341712.9
Median Absolute Deviation (MAD)675419
Skewness1.0483226
Sum6.296256 × 1010
Variance1.853736 × 1012
MonotonicityNot monotonic
2023-06-08T15:14:48.930465image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6452 273
 
0.6%
461790 54
 
0.1%
304294 46
 
0.1%
3080111 42
 
0.1%
337607 36
 
0.1%
185945 34
 
0.1%
1603143 34
 
0.1%
43450 33
 
0.1%
4426497 32
 
0.1%
1270365 32
 
0.1%
Other values (24959) 46311
98.7%
ValueCountFrequency (%)
1 2
< 0.1%
16 2
< 0.1%
75 2
< 0.1%
85 1
 
< 0.1%
99 3
< 0.1%
122 2
< 0.1%
140 1
 
< 0.1%
148 1
 
< 0.1%
153 1
 
< 0.1%
168 4
< 0.1%
ValueCountFrequency (%)
5823993 1
< 0.1%
5808200 1
< 0.1%
5799579 1
< 0.1%
5798032 1
< 0.1%
5797970 1
< 0.1%
5795465 1
< 0.1%
5790845 1
< 0.1%
5785518 1
< 0.1%
5771209 1
< 0.1%
5761104 1
< 0.1%

hotel_country_code
Real number (ℝ)

Distinct126
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.486756
Minimum0
Maximum125
Zeros380
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size183.4 KiB
2023-06-08T15:14:49.094891image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24
Q157
median80
Q3109
95-th percentile118
Maximum125
Range125
Interquartile range (IQR)52

Descriptive statistics

Standard deviation31.100448
Coefficient of variation (CV)0.4066122
Kurtosis-0.83994882
Mean76.486756
Median Absolute Deviation (MAD)29
Skewness-0.24798175
Sum3589294
Variance967.2379
MonotonicityNot monotonic
2023-06-08T15:14:49.262554image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57 7357
15.7%
109 6331
13.5%
80 6136
13.1%
114 4062
 
8.7%
49 2883
 
6.1%
61 2718
 
5.8%
92 2128
 
4.5%
121 2105
 
4.5%
118 1445
 
3.1%
45 1295
 
2.8%
Other values (116) 10467
22.3%
ValueCountFrequency (%)
0 380
0.8%
1 2
 
< 0.1%
2 4
 
< 0.1%
3 10
 
< 0.1%
4 114
 
0.2%
5 835
1.8%
6 1
 
< 0.1%
7 6
 
< 0.1%
8 3
 
< 0.1%
9 5
 
< 0.1%
ValueCountFrequency (%)
125 4
 
< 0.1%
124 3
 
< 0.1%
123 115
 
0.2%
122 2
 
< 0.1%
121 2105
4.5%
120 4
 
< 0.1%
119 1
 
< 0.1%
118 1445
3.1%
117 1
 
< 0.1%
116 18
 
< 0.1%
Distinct17476
Distinct (%)37.2%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
Minimum1999-09-09 00:00:00
Maximum2019-04-18 10:17:00
2023-06-08T15:14:49.434600image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:49.591539image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

hotel_star_rating
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2239968
Minimum-1
Maximum5
Zeros2060
Zeros (%)4.4%
Negative1
Negative (%)< 0.1%
Memory size366.7 KiB
2023-06-08T15:14:49.725119image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q13
median3
Q34
95-th percentile5
Maximum5
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1722628
Coefficient of variation (CV)0.36360543
Kurtosis0.72713638
Mean3.2239968
Median Absolute Deviation (MAD)1
Skewness-0.79401111
Sum151292.5
Variance1.3742
MonotonicityNot monotonic
2023-06-08T15:14:49.830888image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
3 14061
30.0%
4 12531
26.7%
5 5053
 
10.8%
2 5018
 
10.7%
3.5 3389
 
7.2%
0 2060
 
4.4%
2.5 1752
 
3.7%
1 1342
 
2.9%
4.5 1174
 
2.5%
1.5 546
 
1.2%
ValueCountFrequency (%)
-1 1
 
< 0.1%
0 2060
 
4.4%
1 1342
 
2.9%
1.5 546
 
1.2%
2 5018
 
10.7%
2.5 1752
 
3.7%
3 14061
30.0%
3.5 3389
 
7.2%
4 12531
26.7%
4.5 1174
 
2.5%
ValueCountFrequency (%)
5 5053
 
10.8%
4.5 1174
 
2.5%
4 12531
26.7%
3.5 3389
 
7.2%
3 14061
30.0%
2.5 1752
 
3.7%
2 5018
 
10.7%
1.5 546
 
1.2%
1 1342
 
2.9%
0 2060
 
4.4%

accommadation_type_name
Real number (ℝ)

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.395785
Minimum0
Maximum21
Zeros938
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size183.4 KiB
2023-06-08T15:14:49.964490image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q110
median10
Q310
95-th percentile16
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.1764249
Coefficient of variation (CV)0.30554931
Kurtosis2.770737
Mean10.395785
Median Absolute Deviation (MAD)0
Skewness0.1664497
Sum487843
Variance10.089675
MonotonicityNot monotonic
2023-06-08T15:14:50.092041image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
10 34255
73.0%
16 4553
 
9.7%
5 2319
 
4.9%
9 2075
 
4.4%
19 1180
 
2.5%
0 938
 
2.0%
14 454
 
1.0%
17 278
 
0.6%
3 270
 
0.6%
18 238
 
0.5%
Other values (12) 367
 
0.8%
ValueCountFrequency (%)
0 938
2.0%
1 5
 
< 0.1%
2 93
 
0.2%
3 270
 
0.6%
4 1
 
< 0.1%
5 2319
4.9%
6 37
 
0.1%
7 88
 
0.2%
8 1
 
< 0.1%
9 2075
4.4%
ValueCountFrequency (%)
21 65
 
0.1%
20 11
 
< 0.1%
19 1180
 
2.5%
18 238
 
0.5%
17 278
 
0.6%
16 4553
9.7%
15 52
 
0.1%
14 454
 
1.0%
13 8
 
< 0.1%
12 3
 
< 0.1%

charge_option
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
Pay Now
35335 
Pay Later
11567 
Pay at Check-in
 
25

Length

Max length15
Median length7
Mean length7.4972404
Min length7

Characters and Unicode

Total characters351823
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPay Now
2nd rowPay Now
3rd rowPay Now
4th rowPay Now
5th rowPay Later

Common Values

ValueCountFrequency (%)
Pay Now 35335
75.3%
Pay Later 11567
 
24.6%
Pay at Check-in 25
 
0.1%

Length

2023-06-08T15:14:50.236206image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-08T15:14:50.383103image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
pay 46927
50.0%
now 35335
37.6%
later 11567
 
12.3%
at 25
 
< 0.1%
check-in 25
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
a 58519
16.6%
46952
13.3%
P 46927
13.3%
y 46927
13.3%
N 35335
10.0%
o 35335
10.0%
w 35335
10.0%
t 11592
 
3.3%
e 11592
 
3.3%
L 11567
 
3.3%
Other values (8) 11742
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 210992
60.0%
Uppercase Letter 93854
26.7%
Space Separator 46952
 
13.3%
Dash Punctuation 25
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 58519
27.7%
y 46927
22.2%
o 35335
16.7%
w 35335
16.7%
t 11592
 
5.5%
e 11592
 
5.5%
r 11567
 
5.5%
h 25
 
< 0.1%
c 25
 
< 0.1%
k 25
 
< 0.1%
Other values (2) 50
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
P 46927
50.0%
N 35335
37.6%
L 11567
 
12.3%
C 25
 
< 0.1%
Space Separator
ValueCountFrequency (%)
46952
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 304846
86.6%
Common 46977
 
13.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 58519
19.2%
P 46927
15.4%
y 46927
15.4%
N 35335
11.6%
o 35335
11.6%
w 35335
11.6%
t 11592
 
3.8%
e 11592
 
3.8%
L 11567
 
3.8%
r 11567
 
3.8%
Other values (6) 150
 
< 0.1%
Common
ValueCountFrequency (%)
46952
99.9%
- 25
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 351823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 58519
16.6%
46952
13.3%
P 46927
13.3%
y 46927
13.3%
N 35335
10.0%
o 35335
10.0%
w 35335
10.0%
t 11592
 
3.3%
e 11592
 
3.3%
L 11567
 
3.3%
Other values (8) 11742
 
3.3%

h_customer_id
Real number (ℝ)

Distinct24392
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5089212 × 1018
Minimum-9.096408 × 1018
Maximum9.2233353 × 1018
Zeros0
Zeros (%)0.0%
Negative445
Negative (%)0.9%
Memory size366.7 KiB
2023-06-08T15:14:50.520740image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum-9.096408 × 1018
5-th percentile4.0199151 × 1017
Q12.2306526 × 1018
median4.5488106 × 1018
Q36.8535869 × 1018
95-th percentile8.7526647 × 1018
Maximum9.2233353 × 1018
Range-1.2700075 × 1017
Interquartile range (IQR)4.6229344 × 1018

Descriptive statistics

Standard deviation2.8042853 × 1018
Coefficient of variation (CV)0.62194152
Kurtosis0.11193679
Mean4.5089212 × 1018
Median Absolute Deviation (MAD)2.311866 × 1018
Skewness-0.33204114
Sum5.9925552 × 1018
Variance7.8640161 × 1036
MonotonicityNot monotonic
2023-06-08T15:14:50.820719image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.476281626 × 1018258
 
0.5%
9.896276996 × 1017124
 
0.3%
3.403039646 × 1018113
 
0.2%
6.17110326 × 1018108
 
0.2%
2.260038697 × 101894
 
0.2%
2.84968879 × 101865
 
0.1%
6.096800854 × 101862
 
0.1%
7.537464804 × 101851
 
0.1%
6.357426652 × 101749
 
0.1%
7.190936377 × 101849
 
0.1%
Other values (24382) 45954
97.9%
ValueCountFrequency (%)
-9.096407971 × 10181
 
< 0.1%
-9.095945867 × 10181
 
< 0.1%
-9.077138756 × 10183
< 0.1%
-9.063637105 × 10181
 
< 0.1%
-9.029931 × 10182
 
< 0.1%
-9.020452036 × 10186
< 0.1%
-8.961035497 × 10181
 
< 0.1%
-8.931780579 × 10182
 
< 0.1%
-8.908509556 × 10181
 
< 0.1%
-8.896314417 × 10181
 
< 0.1%
ValueCountFrequency (%)
9.223335348 × 10181
 
< 0.1%
9.223298569 × 10184
< 0.1%
9.223219011 × 10181
 
< 0.1%
9.223204826 × 10181
 
< 0.1%
9.222879283 × 10181
 
< 0.1%
9.222675043 × 10182
 
< 0.1%
9.222136928 × 10181
 
< 0.1%
9.221635623 × 10181
 
< 0.1%
9.221401145 × 10188
< 0.1%
9.221262823 × 10181
 
< 0.1%
Distinct136
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
2023-06-08T15:14:51.115384image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Length

Max length32
Median length21
Mean length8.8196134
Min length4

Characters and Unicode

Total characters413878
Distinct characters55
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)0.1%

Sample

1st rowChina
2nd rowJapan
3rd rowTaiwan
4th rowTurkey
5th rowSouth Korea
ValueCountFrequency (%)
south 6364
 
10.0%
korea 6227
 
9.8%
malaysia 6068
 
9.5%
taiwan 5059
 
8.0%
thailand 3409
 
5.4%
united 3078
 
4.8%
china 2669
 
4.2%
japan 2330
 
3.7%
hong 2241
 
3.5%
kong 2241
 
3.5%
Other values (151) 23912
37.6%
2023-06-08T15:14:51.549897image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 68559
16.6%
i 37749
 
9.1%
n 33947
 
8.2%
o 24183
 
5.8%
e 22684
 
5.5%
t 16690
 
4.0%
16667
 
4.0%
r 14661
 
3.5%
s 14628
 
3.5%
h 14514
 
3.5%
Other values (45) 149596
36.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 322079
77.8%
Uppercase Letter 75123
 
18.2%
Space Separator 16671
 
4.0%
Dash Punctuation 3
 
< 0.1%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 68559
21.3%
i 37749
11.7%
n 33947
10.5%
o 24183
 
7.5%
e 22684
 
7.0%
t 16690
 
5.2%
r 14661
 
4.6%
s 14628
 
4.5%
h 14514
 
4.5%
l 14009
 
4.3%
Other values (16) 60455
18.8%
Uppercase Letter
ValueCountFrequency (%)
K 11677
15.5%
S 11036
14.7%
T 8579
11.4%
N 7190
9.6%
M 6499
8.7%
U 5347
7.1%
A 4143
 
5.5%
C 3152
 
4.2%
I 3066
 
4.1%
O 2363
 
3.1%
Other values (15) 12071
16.1%
Space Separator
ValueCountFrequency (%)
16667
> 99.9%
  4
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Other Punctuation
ValueCountFrequency (%)
' 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 397202
96.0%
Common 16676
 
4.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 68559
17.3%
i 37749
 
9.5%
n 33947
 
8.5%
o 24183
 
6.1%
e 22684
 
5.7%
t 16690
 
4.2%
r 14661
 
3.7%
s 14628
 
3.7%
h 14514
 
3.7%
l 14009
 
3.5%
Other values (41) 135578
34.1%
Common
ValueCountFrequency (%)
16667
99.9%
  4
 
< 0.1%
- 3
 
< 0.1%
' 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 413874
> 99.9%
None 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 68559
16.6%
i 37749
 
9.1%
n 33947
 
8.2%
o 24183
 
5.8%
e 22684
 
5.5%
t 16690
 
4.0%
16667
 
4.0%
r 14661
 
3.5%
s 14628
 
3.5%
h 14514
 
3.5%
Other values (44) 149592
36.1%
None
ValueCountFrequency (%)
  4
100.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
0
36872 
1
10055 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters46927
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 36872
78.6%
1 10055
 
21.4%

Length

2023-06-08T15:14:51.698604image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-08T15:14:51.816645image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
0 36872
78.6%
1 10055
 
21.4%

Most occurring characters

ValueCountFrequency (%)
0 36872
78.6%
1 10055
 
21.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46927
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 36872
78.6%
1 10055
 
21.4%

Most occurring scripts

ValueCountFrequency (%)
Common 46927
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 36872
78.6%
1 10055
 
21.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46927
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 36872
78.6%
1 10055
 
21.4%
Distinct144
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
2023-06-08T15:14:51.999865image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Length

Max length32
Median length26
Mean length8.4160718
Min length4

Characters and Unicode

Total characters394941
Distinct characters57
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)0.1%

Sample

1st rowChina
2nd rowJapan
3rd rowTaiwan
4th rowTurkey
5th rowSouth Korea
ValueCountFrequency (%)
south 6765
 
11.2%
korea 6634
 
11.0%
malaysia 6163
 
10.2%
taiwan 5347
 
8.9%
thailand 3514
 
5.8%
united 3211
 
5.3%
china 3075
 
5.1%
japan 2469
 
4.1%
hong 2287
 
3.8%
kong 2287
 
3.8%
Other values (162) 18660
30.9%
2023-06-08T15:14:52.380159image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 69572
17.6%
i 37621
 
9.5%
n 35710
 
9.0%
o 23356
 
5.9%
e 21766
 
5.5%
t 17532
 
4.4%
h 15545
 
3.9%
s 15086
 
3.8%
l 14393
 
3.6%
13481
 
3.4%
Other values (47) 130879
33.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 321012
81.3%
Uppercase Letter 60439
 
15.3%
Space Separator 13485
 
3.4%
Other Punctuation 3
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 69572
21.7%
i 37621
11.7%
n 35710
11.1%
o 23356
 
7.3%
e 21766
 
6.8%
t 17532
 
5.5%
h 15545
 
4.8%
s 15086
 
4.7%
l 14393
 
4.5%
r 13380
 
4.2%
Other values (16) 57051
17.8%
Uppercase Letter
ValueCountFrequency (%)
S 11588
19.2%
K 9947
16.5%
T 8986
14.9%
M 6640
11.0%
C 3587
 
5.9%
U 3249
 
5.4%
I 3217
 
5.3%
J 2481
 
4.1%
H 2298
 
3.8%
A 2284
 
3.8%
Other values (15) 6162
10.2%
Space Separator
ValueCountFrequency (%)
13481
> 99.9%
  4
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
' 2
66.7%
& 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 381451
96.6%
Common 13490
 
3.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 69572
18.2%
i 37621
 
9.9%
n 35710
 
9.4%
o 23356
 
6.1%
e 21766
 
5.7%
t 17532
 
4.6%
h 15545
 
4.1%
s 15086
 
4.0%
l 14393
 
3.8%
r 13380
 
3.5%
Other values (41) 117490
30.8%
Common
ValueCountFrequency (%)
13481
99.9%
  4
 
< 0.1%
' 2
 
< 0.1%
& 1
 
< 0.1%
( 1
 
< 0.1%
) 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 394937
> 99.9%
None 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 69572
17.6%
i 37621
 
9.5%
n 35710
 
9.0%
o 23356
 
5.9%
e 21766
 
5.5%
t 17532
 
4.4%
h 15545
 
3.9%
s 15086
 
3.8%
l 14393
 
3.6%
13481
 
3.4%
Other values (46) 130875
33.1%
None
ValueCountFrequency (%)
  4
100.0%

no_of_adults
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3475611
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-08T15:14:52.529844image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile4
Maximum30
Range29
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3241378
Coefficient of variation (CV)0.56404829
Kurtosis33.488263
Mean2.3475611
Median Absolute Deviation (MAD)0
Skewness4.2137414
Sum110164
Variance1.753341
MonotonicityNot monotonic
2023-06-08T15:14:52.661641image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2 32070
68.3%
1 5481
 
11.7%
4 4492
 
9.6%
3 2905
 
6.2%
6 926
 
2.0%
5 378
 
0.8%
8 301
 
0.6%
10 101
 
0.2%
7 81
 
0.2%
12 67
 
0.1%
Other values (11) 125
 
0.3%
ValueCountFrequency (%)
1 5481
 
11.7%
2 32070
68.3%
3 2905
 
6.2%
4 4492
 
9.6%
5 378
 
0.8%
6 926
 
2.0%
7 81
 
0.2%
8 301
 
0.6%
9 49
 
0.1%
10 101
 
0.2%
ValueCountFrequency (%)
30 1
 
< 0.1%
27 2
 
< 0.1%
20 1
 
< 0.1%
18 18
 
< 0.1%
17 2
 
< 0.1%
16 16
 
< 0.1%
15 4
 
< 0.1%
14 16
 
< 0.1%
13 3
 
< 0.1%
12 67
0.1%

no_of_children
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14814499
Minimum0
Maximum10
Zeros42690
Zeros (%)91.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-08T15:14:52.869361image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.52912642
Coefficient of variation (CV)3.5716795
Kurtosis25.197554
Mean0.14814499
Median Absolute Deviation (MAD)0
Skewness4.4201626
Sum6952
Variance0.27997477
MonotonicityNot monotonic
2023-06-08T15:14:52.978952image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 42690
91.0%
1 2117
 
4.5%
2 1726
 
3.7%
3 251
 
0.5%
4 109
 
0.2%
5 18
 
< 0.1%
6 12
 
< 0.1%
7 2
 
< 0.1%
10 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 42690
91.0%
1 2117
 
4.5%
2 1726
 
3.7%
3 251
 
0.5%
4 109
 
0.2%
5 18
 
< 0.1%
6 12
 
< 0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
8 1
 
< 0.1%
7 2
 
< 0.1%
6 12
 
< 0.1%
5 18
 
< 0.1%
4 109
 
0.2%
3 251
 
0.5%
2 1726
 
3.7%
1 2117
 
4.5%
0 42690
91.0%

no_of_extra_bed
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
0
46416 
1
 
483
2
 
22
3
 
5
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters46927
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 46416
98.9%
1 483
 
1.0%
2 22
 
< 0.1%
3 5
 
< 0.1%
5 1
 
< 0.1%

Length

2023-06-08T15:14:53.096409image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-08T15:14:53.221449image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
0 46416
98.9%
1 483
 
1.0%
2 22
 
< 0.1%
3 5
 
< 0.1%
5 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 46416
98.9%
1 483
 
1.0%
2 22
 
< 0.1%
3 5
 
< 0.1%
5 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46927
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46416
98.9%
1 483
 
1.0%
2 22
 
< 0.1%
3 5
 
< 0.1%
5 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 46927
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46416
98.9%
1 483
 
1.0%
2 22
 
< 0.1%
3 5
 
< 0.1%
5 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46927
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46416
98.9%
1 483
 
1.0%
2 22
 
< 0.1%
3 5
 
< 0.1%
5 1
 
< 0.1%

no_of_room
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.140303
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-08T15:14:53.325104image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.51904538
Coefficient of variation (CV)0.45518197
Kurtosis52.885359
Mean1.140303
Median Absolute Deviation (MAD)0
Skewness5.9691302
Sum53511
Variance0.26940811
MonotonicityNot monotonic
2023-06-08T15:14:53.445063image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 42320
90.2%
2 3426
 
7.3%
3 751
 
1.6%
4 244
 
0.5%
5 106
 
0.2%
6 32
 
0.1%
9 20
 
< 0.1%
7 16
 
< 0.1%
8 12
 
< 0.1%
ValueCountFrequency (%)
1 42320
90.2%
2 3426
 
7.3%
3 751
 
1.6%
4 244
 
0.5%
5 106
 
0.2%
6 32
 
0.1%
7 16
 
< 0.1%
8 12
 
< 0.1%
9 20
 
< 0.1%
ValueCountFrequency (%)
9 20
 
< 0.1%
8 12
 
< 0.1%
7 16
 
< 0.1%
6 32
 
0.1%
5 106
 
0.2%
4 244
 
0.5%
3 751
 
1.6%
2 3426
 
7.3%
1 42320
90.2%
Distinct141
Distinct (%)0.3%
Missing2
Missing (%)< 0.1%
Memory size366.7 KiB
2023-06-08T15:14:53.749936image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters93850
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)< 0.1%

Sample

1st rowCN
2nd rowJP
3rd rowTW
4th rowTR
5th rowKR
ValueCountFrequency (%)
kr 6298
13.4%
my 6185
13.2%
tw 5255
11.2%
th 4308
 
9.2%
jp 2530
 
5.4%
cn 2494
 
5.3%
hk 2424
 
5.2%
id 2263
 
4.8%
us 1960
 
4.2%
sg 1926
 
4.1%
Other values (131) 11282
24.0%
2023-06-08T15:14:54.119555image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 10075
 
10.7%
K 9274
 
9.9%
H 8972
 
9.6%
R 7083
 
7.5%
M 6936
 
7.4%
Y 6207
 
6.6%
W 5316
 
5.7%
N 5056
 
5.4%
S 4588
 
4.9%
P 4583
 
4.9%
Other values (17) 25760
27.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 93784
99.9%
Decimal Number 66
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 10075
 
10.7%
K 9274
 
9.9%
H 8972
 
9.6%
R 7083
 
7.6%
M 6936
 
7.4%
Y 6207
 
6.6%
W 5316
 
5.7%
N 5056
 
5.4%
S 4588
 
4.9%
P 4583
 
4.9%
Other values (16) 25694
27.4%
Decimal Number
ValueCountFrequency (%)
1 66
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 93784
99.9%
Common 66
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 10075
 
10.7%
K 9274
 
9.9%
H 8972
 
9.6%
R 7083
 
7.6%
M 6936
 
7.4%
Y 6207
 
6.6%
W 5316
 
5.7%
N 5056
 
5.4%
S 4588
 
4.9%
P 4583
 
4.9%
Other values (16) 25694
27.4%
Common
ValueCountFrequency (%)
1 66
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93850
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 10075
 
10.7%
K 9274
 
9.9%
H 8972
 
9.6%
R 7083
 
7.5%
M 6936
 
7.4%
Y 6207
 
6.6%
W 5316
 
5.7%
N 5056
 
5.4%
S 4588
 
4.9%
P 4583
 
4.9%
Other values (17) 25760
27.4%

language
Real number (ℝ)

Distinct49
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.379462
Minimum0
Maximum48
Zeros466
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size183.4 KiB
2023-06-08T15:14:54.321448image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q18
median26
Q337
95-th percentile45
Maximum48
Range48
Interquartile range (IQR)29

Descriptive statistics

Standard deviation14.699654
Coefficient of variation (CV)0.62874219
Kurtosis-1.4965756
Mean23.379462
Median Absolute Deviation (MAD)17
Skewness0.21910009
Sum1097128
Variance216.07983
MonotonicityNot monotonic
2023-06-08T15:14:54.495431image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
8 17238
36.7%
27 6684
 
14.2%
43 5776
 
12.3%
37 3217
 
6.9%
26 2436
 
5.2%
45 2399
 
5.1%
44 1929
 
4.1%
16 1293
 
2.8%
24 765
 
1.6%
48 636
 
1.4%
Other values (39) 4554
 
9.7%
ValueCountFrequency (%)
0 466
 
1.0%
1 2
 
< 0.1%
2 13
 
< 0.1%
3 1
 
< 0.1%
4 17
 
< 0.1%
5 63
 
0.1%
6 148
 
0.3%
7 5
 
< 0.1%
8 17238
36.7%
9 480
 
1.0%
ValueCountFrequency (%)
48 636
 
1.4%
47 4
 
< 0.1%
46 65
 
0.1%
45 2399
5.1%
44 1929
 
4.1%
43 5776
12.3%
42 90
 
0.2%
41 6
 
< 0.1%
40 9
 
< 0.1%
39 229
 
0.5%

original_selling_amount
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct26481
Distinct (%)56.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean219.59182
Minimum2.1
Maximum49566.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-08T15:14:54.673602image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum2.1
5-th percentile17.7
Q150.585
median107.78
Q3243.01
95-th percentile750.268
Maximum49566.16
Range49564.06
Interquartile range (IQR)192.425

Descriptive statistics

Standard deviation439.9447
Coefficient of variation (CV)2.0034658
Kurtosis3515.9943
Mean219.59182
Median Absolute Deviation (MAD)72.27
Skewness36.550096
Sum10304785
Variance193551.34
MonotonicityNot monotonic
2023-06-08T15:14:54.848603image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122.63 14
 
< 0.1%
15 12
 
< 0.1%
37.05 12
 
< 0.1%
22 11
 
< 0.1%
45 11
 
< 0.1%
19.56 11
 
< 0.1%
222.94 10
 
< 0.1%
28.46 10
 
< 0.1%
30 10
 
< 0.1%
16.86 10
 
< 0.1%
Other values (26471) 46816
99.8%
ValueCountFrequency (%)
2.1 1
< 0.1%
2.27 1
< 0.1%
2.31 1
< 0.1%
2.5 1
< 0.1%
2.59 1
< 0.1%
2.64 1
< 0.1%
2.76 1
< 0.1%
2.8 1
< 0.1%
2.82 1
< 0.1%
2.9 1
< 0.1%
ValueCountFrequency (%)
49566.16 1
< 0.1%
17942.82 1
< 0.1%
15430.5 1
< 0.1%
13015.52 1
< 0.1%
11672.15 1
< 0.1%
9562.86 1
< 0.1%
9074.52 1
< 0.1%
9065.76 1
< 0.1%
8024.56 1
< 0.1%
7437.78 1
< 0.1%
Distinct36
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
Visa
23088 
MasterCard
13875 
UNKNOWN
3210 
American Express
 
2198
JCB
 
1124
Other values (31)
3432 

Length

Max length21
Median length20
Mean length6.8831163
Min length3

Characters and Unicode

Total characters323004
Distinct characters49
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAlipay
2nd rowAmerican Express
3rd rowVisa
4th rowMasterCard
5th rowVisa

Common Values

ValueCountFrequency (%)
Visa 23088
49.2%
MasterCard 13875
29.6%
UNKNOWN 3210
 
6.8%
American Express 2198
 
4.7%
JCB 1124
 
2.4%
Alipay 945
 
2.0%
PayPal 557
 
1.2%
MayBank2U 430
 
0.9%
UnionPay - Creditcard 292
 
0.6%
K PLUS 206
 
0.4%
Other values (26) 1002
 
2.1%

Length

2023-06-08T15:14:55.008566image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
visa 23088
45.5%
mastercard 13875
27.3%
unknown 3210
 
6.3%
american 2198
 
4.3%
express 2198
 
4.3%
jcb 1124
 
2.2%
alipay 945
 
1.9%
paypal 557
 
1.1%
maybank2u 430
 
0.8%
unionpay 339
 
0.7%
Other values (43) 2784
 
5.5%

Most occurring characters

ValueCountFrequency (%)
a 57861
17.9%
s 41874
13.0%
r 32986
10.2%
i 27345
8.5%
V 23088
 
7.1%
e 19220
 
6.0%
C 15988
 
4.9%
M 14788
 
4.6%
t 14581
 
4.5%
d 14574
 
4.5%
Other values (39) 60699
18.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 228931
70.9%
Uppercase Letter 89464
 
27.7%
Space Separator 3821
 
1.2%
Decimal Number 449
 
0.1%
Dash Punctuation 339
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 57861
25.3%
s 41874
18.3%
r 32986
14.4%
i 27345
11.9%
e 19220
 
8.4%
t 14581
 
6.4%
d 14574
 
6.4%
n 3570
 
1.6%
p 3303
 
1.4%
c 2737
 
1.2%
Other values (13) 10880
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
V 23088
25.8%
C 15988
17.9%
M 14788
16.5%
N 9652
10.8%
U 4185
 
4.7%
K 3449
 
3.9%
W 3411
 
3.8%
A 3382
 
3.8%
O 3222
 
3.6%
E 2248
 
2.5%
Other values (12) 6051
 
6.8%
Decimal Number
ValueCountFrequency (%)
2 430
95.8%
7 19
 
4.2%
Space Separator
ValueCountFrequency (%)
3821
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 339
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 318395
98.6%
Common 4609
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 57861
18.2%
s 41874
13.2%
r 32986
10.4%
i 27345
8.6%
V 23088
 
7.3%
e 19220
 
6.0%
C 15988
 
5.0%
M 14788
 
4.6%
t 14581
 
4.6%
d 14574
 
4.6%
Other values (35) 56090
17.6%
Common
ValueCountFrequency (%)
3821
82.9%
2 430
 
9.3%
- 339
 
7.4%
7 19
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 323004
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 57861
17.9%
s 41874
13.0%
r 32986
10.2%
i 27345
8.5%
V 23088
 
7.1%
e 19220
 
6.0%
C 15988
 
4.9%
M 14788
 
4.6%
t 14581
 
4.5%
d 14574
 
4.5%
Other values (39) 60699
18.8%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
Credit Card
46132 
Invoice
 
612
Gift Card
 
183

Length

Max length11
Median length11
Mean length10.940035
Min length7

Characters and Unicode

Total characters513383
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCredit Card
2nd rowCredit Card
3rd rowCredit Card
4th rowCredit Card
5th rowCredit Card

Common Values

ValueCountFrequency (%)
Credit Card 46132
98.3%
Invoice 612
 
1.3%
Gift Card 183
 
0.4%

Length

2023-06-08T15:14:55.159619image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-08T15:14:55.317425image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
card 46315
49.7%
credit 46132
49.5%
invoice 612
 
0.7%
gift 183
 
0.2%

Most occurring characters

ValueCountFrequency (%)
C 92447
18.0%
r 92447
18.0%
d 92447
18.0%
i 46927
9.1%
e 46744
9.1%
t 46315
9.0%
46315
9.0%
a 46315
9.0%
I 612
 
0.1%
n 612
 
0.1%
Other values (5) 2202
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 373826
72.8%
Uppercase Letter 93242
 
18.2%
Space Separator 46315
 
9.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 92447
24.7%
d 92447
24.7%
i 46927
12.6%
e 46744
12.5%
t 46315
12.4%
a 46315
12.4%
n 612
 
0.2%
v 612
 
0.2%
o 612
 
0.2%
c 612
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
C 92447
99.1%
I 612
 
0.7%
G 183
 
0.2%
Space Separator
ValueCountFrequency (%)
46315
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 467068
91.0%
Common 46315
 
9.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 92447
19.8%
r 92447
19.8%
d 92447
19.8%
i 46927
10.0%
e 46744
10.0%
t 46315
9.9%
a 46315
9.9%
I 612
 
0.1%
n 612
 
0.1%
v 612
 
0.1%
Other values (4) 1590
 
0.3%
Common
ValueCountFrequency (%)
46315
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 513383
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 92447
18.0%
r 92447
18.0%
d 92447
18.0%
i 46927
9.1%
e 46744
9.1%
t 46315
9.0%
46315
9.0%
a 46315
9.0%
I 612
 
0.1%
n 612
 
0.1%
Other values (5) 2202
 
0.4%
Distinct50
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
MYR
6088 
KRW
5995 
TWD
5300 
USD
3685 
THB
3604 
Other values (45)
22255 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters140781
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCNY
2nd rowJPY
3rd rowTWD
4th rowTRY
5th rowKRW

Common Values

ValueCountFrequency (%)
MYR 6088
13.0%
KRW 5995
12.8%
TWD 5300
11.3%
USD 3685
 
7.9%
THB 3604
 
7.7%
CNY 2908
 
6.2%
HKD 2695
 
5.7%
JPY 2677
 
5.7%
SGD 1977
 
4.2%
IDR 1908
 
4.1%
Other values (40) 10090
21.5%

Length

2023-06-08T15:14:55.445679image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
myr 6088
13.0%
krw 5995
12.8%
twd 5300
11.3%
usd 3685
 
7.9%
thb 3604
 
7.7%
cny 2908
 
6.2%
hkd 2695
 
5.7%
jpy 2677
 
5.7%
sgd 1977
 
4.2%
idr 1908
 
4.1%
Other values (40) 10090
21.5%

Most occurring characters

ValueCountFrequency (%)
D 18718
13.3%
R 17659
12.5%
Y 11789
 
8.4%
W 11352
 
8.1%
K 9238
 
6.6%
T 9034
 
6.4%
H 8063
 
5.7%
U 7111
 
5.1%
P 6972
 
5.0%
S 6444
 
4.6%
Other values (16) 34401
24.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 140781
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 18718
13.3%
R 17659
12.5%
Y 11789
 
8.4%
W 11352
 
8.1%
K 9238
 
6.6%
T 9034
 
6.4%
H 8063
 
5.7%
U 7111
 
5.1%
P 6972
 
5.0%
S 6444
 
4.6%
Other values (16) 34401
24.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 140781
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 18718
13.3%
R 17659
12.5%
Y 11789
 
8.4%
W 11352
 
8.1%
K 9238
 
6.6%
T 9034
 
6.4%
H 8063
 
5.7%
U 7111
 
5.1%
P 6972
 
5.0%
S 6444
 
4.6%
Other values (16) 34401
24.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140781
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 18718
13.3%
R 17659
12.5%
Y 11789
 
8.4%
W 11352
 
8.1%
K 9238
 
6.6%
T 9034
 
6.4%
H 8063
 
5.7%
U 7111
 
5.1%
P 6972
 
5.0%
S 6444
 
4.6%
Other values (16) 34401
24.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.0 KiB
True
30549 
False
16378 
ValueCountFrequency (%)
True 30549
65.1%
False 16378
34.9%
2023-06-08T15:14:55.593868image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Distinct719
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
2023-06-08T15:14:55.885277image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Length

Max length20
Median length19
Mean length9.8295864
Min length4

Characters and Unicode

Total characters461273
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique348 ?
Unique (%)0.7%

Sample

1st row1D1N_1N
2nd row3D1N_1N
3rd row1D1N_1N
4th row365D100P_100P
5th row3D100P_100P
ValueCountFrequency (%)
365d100p_100p 12793
27.3%
1d1n_1n 7026
15.0%
3d1n_1n 2712
 
5.8%
1d100p 2008
 
4.3%
3d1n_100p 1984
 
4.2%
1d100p_100p 1361
 
2.9%
7d100p_100p 1258
 
2.7%
3d100p_100p 1220
 
2.6%
2d100p 1175
 
2.5%
3d100p 1125
 
2.4%
Other values (709) 14265
30.4%
2023-06-08T15:14:56.552830image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 116602
25.3%
1 98713
21.4%
P 58967
12.8%
D 48674
10.6%
_ 41142
 
8.9%
N 30034
 
6.5%
3 22793
 
4.9%
5 16343
 
3.5%
6 13603
 
2.9%
2 4778
 
1.0%
Other values (8) 9624
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 280592
60.8%
Uppercase Letter 139539
30.3%
Connector Punctuation 41142
 
8.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 116602
41.6%
1 98713
35.2%
3 22793
 
8.1%
5 16343
 
5.8%
6 13603
 
4.8%
2 4778
 
1.7%
7 4537
 
1.6%
4 2485
 
0.9%
8 454
 
0.2%
9 284
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
P 58967
42.3%
D 48674
34.9%
N 30034
21.5%
U 466
 
0.3%
K 466
 
0.3%
O 466
 
0.3%
W 466
 
0.3%
Connector Punctuation
ValueCountFrequency (%)
_ 41142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 321734
69.7%
Latin 139539
30.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 116602
36.2%
1 98713
30.7%
_ 41142
 
12.8%
3 22793
 
7.1%
5 16343
 
5.1%
6 13603
 
4.2%
2 4778
 
1.5%
7 4537
 
1.4%
4 2485
 
0.8%
8 454
 
0.1%
Latin
ValueCountFrequency (%)
P 58967
42.3%
D 48674
34.9%
N 30034
21.5%
U 466
 
0.3%
K 466
 
0.3%
O 466
 
0.3%
W 466
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 461273
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 116602
25.3%
1 98713
21.4%
P 58967
12.8%
D 48674
10.6%
_ 41142
 
8.9%
N 30034
 
6.5%
3 22793
 
4.9%
5 16343
 
3.5%
6 13603
 
2.9%
2 4778
 
1.0%
Other values (8) 9624
 
2.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.0 KiB
False
36484 
True
10443 
ValueCountFrequency (%)
False 36484
77.7%
True 10443
 
22.3%
2023-06-08T15:14:56.767918image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing20030
Missing (%)42.7%
Memory size366.7 KiB
1.0
19328 
0.0
7569 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters80691
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 19328
41.2%
0.0 7569
 
16.1%
(Missing) 20030
42.7%

Length

2023-06-08T15:14:56.973135image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-08T15:14:57.264551image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 19328
71.9%
0.0 7569
 
28.1%

Most occurring characters

ValueCountFrequency (%)
0 34466
42.7%
. 26897
33.3%
1 19328
24.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 53794
66.7%
Other Punctuation 26897
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 34466
64.1%
1 19328
35.9%
Other Punctuation
ValueCountFrequency (%)
. 26897
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 80691
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 34466
42.7%
. 26897
33.3%
1 19328
24.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80691
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 34466
42.7%
. 26897
33.3%
1 19328
24.0%

request_latecheckin
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing20030
Missing (%)42.7%
Memory size366.7 KiB
0.0
26195 
1.0
 
702

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters80691
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 26195
55.8%
1.0 702
 
1.5%
(Missing) 20030
42.7%

Length

2023-06-08T15:14:57.419620image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-08T15:14:57.573038image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 26195
97.4%
1.0 702
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 53092
65.8%
. 26897
33.3%
1 702
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 53794
66.7%
Other Punctuation 26897
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 53092
98.7%
1 702
 
1.3%
Other Punctuation
ValueCountFrequency (%)
. 26897
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 80691
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 53092
65.8%
. 26897
33.3%
1 702
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80691
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 53092
65.8%
. 26897
33.3%
1 702
 
0.9%
Distinct2
Distinct (%)< 0.1%
Missing20030
Missing (%)42.7%
Memory size366.7 KiB
0.0
22844 
1.0
4053 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters80691
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 22844
48.7%
1.0 4053
 
8.6%
(Missing) 20030
42.7%

Length

2023-06-08T15:14:57.752766image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-08T15:14:58.002093image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 22844
84.9%
1.0 4053
 
15.1%

Most occurring characters

ValueCountFrequency (%)
0 49741
61.6%
. 26897
33.3%
1 4053
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 53794
66.7%
Other Punctuation 26897
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 49741
92.5%
1 4053
 
7.5%
Other Punctuation
ValueCountFrequency (%)
. 26897
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 80691
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 49741
61.6%
. 26897
33.3%
1 4053
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80691
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 49741
61.6%
. 26897
33.3%
1 4053
 
5.0%

request_largebed
Categorical

Distinct2
Distinct (%)< 0.1%
Missing20030
Missing (%)42.7%
Memory size366.7 KiB
0.0
16391 
1.0
10506 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters80691
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 16391
34.9%
1.0 10506
22.4%
(Missing) 20030
42.7%

Length

2023-06-08T15:14:58.106999image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-08T15:14:58.273350image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 16391
60.9%
1.0 10506
39.1%

Most occurring characters

ValueCountFrequency (%)
0 43288
53.6%
. 26897
33.3%
1 10506
 
13.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 53794
66.7%
Other Punctuation 26897
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 43288
80.5%
1 10506
 
19.5%
Other Punctuation
ValueCountFrequency (%)
. 26897
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 80691
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 43288
53.6%
. 26897
33.3%
1 10506
 
13.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80691
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 43288
53.6%
. 26897
33.3%
1 10506
 
13.0%

request_twinbeds
Categorical

Distinct2
Distinct (%)< 0.1%
Missing20030
Missing (%)42.7%
Memory size366.7 KiB
0.0
22624 
1.0
4273 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters80691
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 22624
48.2%
1.0 4273
 
9.1%
(Missing) 20030
42.7%

Length

2023-06-08T15:14:58.400172image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-08T15:14:58.562749image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 22624
84.1%
1.0 4273
 
15.9%

Most occurring characters

ValueCountFrequency (%)
0 49521
61.4%
. 26897
33.3%
1 4273
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 53794
66.7%
Other Punctuation 26897
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 49521
92.1%
1 4273
 
7.9%
Other Punctuation
ValueCountFrequency (%)
. 26897
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 80691
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 49521
61.4%
. 26897
33.3%
1 4273
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80691
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 49521
61.4%
. 26897
33.3%
1 4273
 
5.3%

request_airport
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing20030
Missing (%)42.7%
Memory size366.7 KiB
0.0
26698 
1.0
 
199

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters80691
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 26698
56.9%
1.0 199
 
0.4%
(Missing) 20030
42.7%

Length

2023-06-08T15:14:58.688691image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-08T15:14:58.848366image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 26698
99.3%
1.0 199
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 53595
66.4%
. 26897
33.3%
1 199
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 53794
66.7%
Other Punctuation 26897
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 53595
99.6%
1 199
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 26897
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 80691
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 53595
66.4%
. 26897
33.3%
1 199
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80691
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 53595
66.4%
. 26897
33.3%
1 199
 
0.2%

request_earlycheckin
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing20030
Missing (%)42.7%
Memory size366.7 KiB
0.0
25996 
1.0
 
901

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters80691
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 25996
55.4%
1.0 901
 
1.9%
(Missing) 20030
42.7%

Length

2023-06-08T15:14:58.955791image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-08T15:14:59.098048image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 25996
96.7%
1.0 901
 
3.3%

Most occurring characters

ValueCountFrequency (%)
0 52893
65.6%
. 26897
33.3%
1 901
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 53794
66.7%
Other Punctuation 26897
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 52893
98.3%
1 901
 
1.7%
Other Punctuation
ValueCountFrequency (%)
. 26897
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 80691
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 52893
65.6%
. 26897
33.3%
1 901
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80691
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 52893
65.6%
. 26897
33.3%
1 901
 
1.1%

hotel_area_code
Real number (ℝ)

Distinct5057
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3025.0395
Minimum1
Maximum5896
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-08T15:14:59.381723image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile243
Q11477
median3134
Q34572
95-th percentile5602
Maximum5896
Range5895
Interquartile range (IQR)3095

Descriptive statistics

Standard deviation1733.8559
Coefficient of variation (CV)0.573168
Kurtosis-1.2487545
Mean3025.0395
Median Absolute Deviation (MAD)1549
Skewness-0.11513251
Sum1.4195603 × 108
Variance3006256.2
MonotonicityNot monotonic
2023-06-08T15:14:59.537467image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3747 718
 
1.5%
1192 571
 
1.2%
643 419
 
0.9%
4463 404
 
0.9%
606 382
 
0.8%
104 372
 
0.8%
4364 342
 
0.7%
3156 335
 
0.7%
2553 322
 
0.7%
5891 302
 
0.6%
Other values (5047) 42760
91.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 7
< 0.1%
5 1
 
< 0.1%
6 5
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 7
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
5896 1
 
< 0.1%
5894 2
 
< 0.1%
5893 1
 
< 0.1%
5892 2
 
< 0.1%
5891 302
0.6%
5890 1
 
< 0.1%
5889 18
 
< 0.1%
5888 1
 
< 0.1%
5887 39
 
0.1%
5886 10
 
< 0.1%

hotel_brand_code
Real number (ℝ)

Distinct850
Distinct (%)7.0%
Missing34699
Missing (%)73.9%
Infinite0
Infinite (%)0.0%
Mean479.07458
Minimum0
Maximum936
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-08T15:14:59.743891image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q1253
median481
Q3741
95-th percentile914
Maximum936
Range936
Interquartile range (IQR)488

Descriptive statistics

Standard deviation277.61635
Coefficient of variation (CV)0.57948461
Kurtosis-1.1793446
Mean479.07458
Median Absolute Deviation (MAD)243
Skewness-0.0051064914
Sum5858124
Variance77070.837
MonotonicityNot monotonic
2023-06-08T15:14:59.927656image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
777 293
 
0.6%
442 254
 
0.5%
51 222
 
0.5%
253 215
 
0.5%
593 205
 
0.4%
918 195
 
0.4%
520 176
 
0.4%
50 160
 
0.3%
789 157
 
0.3%
193 155
 
0.3%
Other values (840) 10196
 
21.7%
(Missing) 34699
73.9%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 3
 
< 0.1%
2 3
 
< 0.1%
3 35
0.1%
4 2
 
< 0.1%
6 13
 
< 0.1%
7 5
 
< 0.1%
8 3
 
< 0.1%
9 65
0.1%
10 28
0.1%
ValueCountFrequency (%)
936 66
0.1%
935 11
 
< 0.1%
934 7
 
< 0.1%
933 58
0.1%
932 7
 
< 0.1%
931 28
0.1%
930 2
 
< 0.1%
929 1
 
< 0.1%
928 4
 
< 0.1%
927 2
 
< 0.1%

hotel_chain_code
Real number (ℝ)

Distinct610
Distinct (%)4.8%
Missing34343
Missing (%)73.2%
Infinite0
Infinite (%)0.0%
Mean357.64781
Minimum0
Maximum680
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-08T15:15:00.234773image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28
Q1181
median355
Q3575
95-th percentile672
Maximum680
Range680
Interquartile range (IQR)394

Descriptive statistics

Standard deviation208.81905
Coefficient of variation (CV)0.58386783
Kurtosis-1.3361399
Mean357.64781
Median Absolute Deviation (MAD)209
Skewness-0.058302719
Sum4500640
Variance43605.395
MonotonicityNot monotonic
2023-06-08T15:15:00.430787image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 696
 
1.5%
296 607
 
1.3%
675 383
 
0.8%
55 363
 
0.8%
181 340
 
0.7%
386 279
 
0.6%
537 249
 
0.5%
587 232
 
0.5%
217 222
 
0.5%
583 214
 
0.5%
Other values (600) 8999
 
19.2%
(Missing) 34343
73.2%
ValueCountFrequency (%)
0 8
 
< 0.1%
1 50
0.1%
2 39
0.1%
3 1
 
< 0.1%
4 2
 
< 0.1%
5 2
 
< 0.1%
7 3
 
< 0.1%
8 5
 
< 0.1%
9 1
 
< 0.1%
10 50
0.1%
ValueCountFrequency (%)
680 3
 
< 0.1%
679 52
 
0.1%
678 117
 
0.2%
677 13
 
< 0.1%
676 2
 
< 0.1%
675 383
0.8%
674 15
 
< 0.1%
673 27
 
0.1%
672 26
 
0.1%
671 2
 
< 0.1%

hotel_city_code
Real number (ℝ)

Distinct2402
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1498.0466
Minimum0
Maximum2808
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-08T15:15:00.646179image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile140
Q1583
median1572
Q32310
95-th percentile2797
Maximum2808
Range2808
Interquartile range (IQR)1727

Descriptive statistics

Standard deviation909.02087
Coefficient of variation (CV)0.60680414
Kurtosis-1.4051834
Mean1498.0466
Median Absolute Deviation (MAD)875
Skewness-0.16917617
Sum70298832
Variance826318.94
MonotonicityNot monotonic
2023-06-08T15:15:00.833719image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2477 2525
 
5.4%
2797 1883
 
4.0%
1403 1794
 
3.8%
142 1295
 
2.8%
162 1163
 
2.5%
2249 1124
 
2.4%
437 1059
 
2.3%
2799 987
 
2.1%
1816 881
 
1.9%
2310 767
 
1.6%
Other values (2392) 33449
71.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 2
 
< 0.1%
3 10
 
< 0.1%
4 2
 
< 0.1%
5 20
< 0.1%
6 3
 
< 0.1%
8 29
0.1%
9 1
 
< 0.1%
10 2
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
2808 2
 
< 0.1%
2807 3
 
< 0.1%
2806 1
 
< 0.1%
2805 1
 
< 0.1%
2804 2
 
< 0.1%
2803 1
 
< 0.1%
2802 1
 
< 0.1%
2800 10
 
< 0.1%
2799 987
2.1%
2797 1883
4.0%
Distinct347
Distinct (%)2.7%
Missing34250
Missing (%)73.0%
Memory size366.7 KiB
Minimum2017-08-12 00:00:00
Maximum2019-03-09 00:00:00
2023-06-08T15:15:00.995046image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:15:01.172876image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

did_cancel
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.0 KiB
False
34250 
True
12677 
ValueCountFrequency (%)
False 34250
73.0%
True 12677
 
27.0%
2023-06-08T15:15:01.351491image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

distance_booking_checkin
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct345
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.429284
Minimum-1
Maximum447
Zeros11952
Zeros (%)25.5%
Negative328
Negative (%)0.7%
Memory size183.4 KiB
2023-06-08T15:15:01.501223image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median9
Q337
95-th percentile132
Maximum447
Range448
Interquartile range (IQR)37

Descriptive statistics

Standard deviation46.739778
Coefficient of variation (CV)1.5882064
Kurtosis7.9746826
Mean29.429284
Median Absolute Deviation (MAD)9
Skewness2.5657421
Sum1381028
Variance2184.6069
MonotonicityNot monotonic
2023-06-08T15:15:01.660110image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11952
25.5%
1 2239
 
4.8%
2 1782
 
3.8%
3 1428
 
3.0%
4 1229
 
2.6%
5 1086
 
2.3%
6 996
 
2.1%
7 847
 
1.8%
8 838
 
1.8%
9 792
 
1.7%
Other values (335) 23738
50.6%
ValueCountFrequency (%)
-1 328
 
0.7%
0 11952
25.5%
1 2239
 
4.8%
2 1782
 
3.8%
3 1428
 
3.0%
4 1229
 
2.6%
5 1086
 
2.3%
6 996
 
2.1%
7 847
 
1.8%
8 838
 
1.8%
ValueCountFrequency (%)
447 1
 
< 0.1%
361 3
< 0.1%
359 1
 
< 0.1%
357 1
 
< 0.1%
356 1
 
< 0.1%
355 1
 
< 0.1%
353 1
 
< 0.1%
350 1
 
< 0.1%
349 2
< 0.1%
347 1
 
< 0.1%

amount_guests
Real number (ℝ)

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4957061
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-08T15:15:01.800423image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile5
Maximum30
Range29
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4588569
Coefficient of variation (CV)0.58454674
Kurtosis26.962429
Mean2.4957061
Median Absolute Deviation (MAD)0
Skewness3.7545332
Sum117116
Variance2.1282633
MonotonicityNot monotonic
2023-06-08T15:15:01.944978image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2 29487
62.8%
4 5766
 
12.3%
1 5140
 
11.0%
3 3851
 
8.2%
6 1116
 
2.4%
5 707
 
1.5%
8 353
 
0.8%
7 125
 
0.3%
10 120
 
0.3%
9 77
 
0.2%
Other values (13) 185
 
0.4%
ValueCountFrequency (%)
1 5140
 
11.0%
2 29487
62.8%
3 3851
 
8.2%
4 5766
 
12.3%
5 707
 
1.5%
6 1116
 
2.4%
7 125
 
0.3%
8 353
 
0.8%
9 77
 
0.2%
10 120
 
0.3%
ValueCountFrequency (%)
30 1
 
< 0.1%
27 2
 
< 0.1%
24 1
 
< 0.1%
21 1
 
< 0.1%
20 6
 
< 0.1%
18 17
< 0.1%
17 3
 
< 0.1%
16 16
< 0.1%
15 6
 
< 0.1%
14 22
< 0.1%

amount_nights
Real number (ℝ)

Distinct29
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.993671
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size183.4 KiB
2023-06-08T15:15:02.087060image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum30
Range29
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.7459906
Coefficient of variation (CV)0.87576665
Kurtosis44.530156
Mean1.993671
Median Absolute Deviation (MAD)0
Skewness4.7506128
Sum93557
Variance3.0484831
MonotonicityNot monotonic
2023-06-08T15:15:02.304433image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 25336
54.0%
2 10657
22.7%
3 5353
 
11.4%
4 2625
 
5.6%
5 1275
 
2.7%
6 550
 
1.2%
7 513
 
1.1%
8 158
 
0.3%
10 116
 
0.2%
9 101
 
0.2%
Other values (19) 243
 
0.5%
ValueCountFrequency (%)
1 25336
54.0%
2 10657
22.7%
3 5353
 
11.4%
4 2625
 
5.6%
5 1275
 
2.7%
6 550
 
1.2%
7 513
 
1.1%
8 158
 
0.3%
9 101
 
0.2%
10 116
 
0.2%
ValueCountFrequency (%)
30 11
< 0.1%
29 1
 
< 0.1%
28 7
< 0.1%
27 2
 
< 0.1%
26 2
 
< 0.1%
25 1
 
< 0.1%
23 3
 
< 0.1%
22 1
 
< 0.1%
21 10
< 0.1%
20 12
< 0.1%

price_per_guest_per_night
Real number (ℝ)

Distinct26848
Distinct (%)57.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.164547
Minimum0.083333333
Maximum1770.22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-08T15:15:02.531654image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0.083333333
5-th percentile7.805
Q117.13875
median31.22
Q354.9525
95-th percentile122.70175
Maximum1770.22
Range1770.1367
Interquartile range (IQR)37.81375

Descriptive statistics

Standard deviation45.586819
Coefficient of variation (CV)1.0322039
Kurtosis80.290278
Mean44.164547
Median Absolute Deviation (MAD)16.725
Skewness5.1722562
Sum2072509.7
Variance2078.1581
MonotonicityNot monotonic
2023-06-08T15:15:02.696087image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.5 16
 
< 0.1%
9.78 15
 
< 0.1%
6 14
 
< 0.1%
26.17 14
 
< 0.1%
7 13
 
< 0.1%
15 13
 
< 0.1%
122.63 13
 
< 0.1%
13.5 13
 
< 0.1%
7.5 13
 
< 0.1%
14.23 12
 
< 0.1%
Other values (26838) 46791
99.7%
ValueCountFrequency (%)
0.08333333333 1
< 0.1%
0.1923333333 1
< 0.1%
0.4233333333 1
< 0.1%
0.4875 1
< 0.1%
0.545 1
< 0.1%
0.570625 1
< 0.1%
0.6796875 1
< 0.1%
0.8333333333 1
< 0.1%
0.9225 1
< 0.1%
1 1
< 0.1%
ValueCountFrequency (%)
1770.22 1
< 0.1%
1167.215 1
< 0.1%
915.3983333 1
< 0.1%
813.24 1
< 0.1%
739.645 1
< 0.1%
733.425 1
< 0.1%
730.28 1
< 0.1%
717.035 1
< 0.1%
715.705 1
< 0.1%
686.415 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.0 KiB
True
41347 
False
5580 
ValueCountFrequency (%)
True 41347
88.1%
False 5580
 
11.9%
2023-06-08T15:15:02.903619image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

pay_now
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.0 KiB
True
35335 
False
11592 
ValueCountFrequency (%)
True 35335
75.3%
False 11592
 
24.7%
2023-06-08T15:15:03.070860image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Interactions

2023-06-08T15:14:41.159303image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:23.924829image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:28.377931image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:32.374078image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:35.980700image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:39.544509image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:43.157978image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:47.219698image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:50.719031image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:54.274030image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:58.074368image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:01.695663image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:05.164644image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:08.817046image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:12.260699image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:15.940597image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:19.729836image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:23.088753image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:26.726820image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:30.314427image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:34.060552image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:37.565617image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:41.338835image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:24.146437image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:28.558628image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:32.549657image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:36.158195image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:39.721364image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:43.389986image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:47.384016image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:50.894138image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:54.455511image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:58.250221image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:01.864380image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:05.344995image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:08.992510image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:12.442538image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:16.113375image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:19.892951image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:23.254530image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:26.895139image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:30.484419image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:34.238718image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:37.732257image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:41.514609image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:24.342415image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:28.775277image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:32.711011image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:36.314694image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:39.887824image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:43.550945image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:47.562545image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:51.057801image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:54.652575image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:58.415918image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:02.021014image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:05.508399image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:09.151899image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:12.600597image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:16.277871image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:20.047048image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:23.418165image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:27.058504image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:30.644485image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:34.404491image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:37.894405image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:41.671636image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:24.517869image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:29.013217image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:32.861085image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:36.466197image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:40.057497image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:43.724381image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:47.715929image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:51.251898image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:54.851346image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:58.587152image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:02.178914image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:05.662347image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:09.316499image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:12.763313image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:16.432084image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:20.185600image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:23.561994image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:27.214356image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:30.792424image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:34.562396image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:38.051026image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:41.819211image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:24.687472image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:29.197719image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:33.015610image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:36.610395image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:40.214842image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:43.876429image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:47.858397image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:51.399991image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:55.040728image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:58.730719image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:02.316921image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:05.814404image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:09.456547image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:12.909822image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:16.747382image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:20.329420image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:23.712922image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:27.361000image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:30.940334image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:34.745735image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:38.196957image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:41.989590image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:24.874608image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:29.371749image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:33.177804image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:36.774596image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:40.373499image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:44.044563image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:48.028096image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:51.551603image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:55.218449image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:58.888805image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:02.473506image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:05.976075image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:09.616583image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:13.074751image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:16.899894image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:20.479348image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:23.862978image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:27.519893image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:31.104577image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:34.901894image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:38.352412image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:42.161012image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:25.076889image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:29.535178image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:33.391424image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:36.938603image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:40.528795image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:44.202389image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:48.194504image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:51.705882image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:55.389741image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:59.051911image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:02.619994image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:06.135205image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:09.775708image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:13.228505image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:17.066886image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:20.630656image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:24.017283image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:27.677290image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:31.261155image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:35.055347image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:38.521508image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:42.337388image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:25.260994image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:29.827842image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:33.584604image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:37.229215image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:40.690243image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:44.361630image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:48.348923image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:51.851573image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:55.551758image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:59.207006image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:02.778944image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:06.286737image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:09.928113image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:13.386928image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:17.261546image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:20.775922image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:24.174859image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:27.832749image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:31.416914image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:35.219055image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:38.676516image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:42.512084image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:25.432350image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:29.997816image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:33.755355image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:37.375051image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:40.839819image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:44.523447image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:48.500038image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:52.002117image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:55.722729image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:59.351847image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:02.917434image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:06.446763image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:10.075440image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:13.538245image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:17.414607image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:20.912146image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:24.317159image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:27.984954image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:31.565353image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:35.363733image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:38.819672image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:42.696714image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:25.678783image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:30.183063image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:33.926306image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:37.542446image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:41.014359image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:45.005770image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:48.678632image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:52.177862image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:55.907969image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:59.519969image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:03.108131image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:06.616069image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:10.261234image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:13.713808image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:17.601793image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:21.083935image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:24.484013image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:28.153874image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:31.781471image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:35.543837image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:38.995701image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:42.866714image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:25.995084image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:30.348698image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:34.083713image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:37.693170image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:41.174016image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:45.187667image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:48.833014image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:52.323359image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:56.076391image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:59.675655image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:03.252285image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:06.778039image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:10.415958image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:13.862829image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:17.789940image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:21.284745image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:24.783891image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:28.354834image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:31.951430image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:35.694304image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:39.151399image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:43.022659image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:26.175378image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:30.504002image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:34.243700image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:37.846772image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:41.319532image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:45.365508image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:48.984836image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:52.476318image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:56.236666image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:59.818981image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:03.401566image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:06.919215image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:10.561568image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:14.026190image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:17.975544image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:21.459141image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:24.941489image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:28.504413image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:32.096656image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:35.850263image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:39.300191image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:43.197699image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:26.366266image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:30.683639image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:34.407918image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:38.021523image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:41.486186image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:45.535917image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:49.162430image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:52.631670image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:56.413812image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:59.984343image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:03.558249image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:07.084334image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:10.727420image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:14.211855image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:18.147790image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:21.613993image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:25.100434image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:28.670677image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:32.260504image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:36.015676image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:39.462141image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:43.356574image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:26.541261image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:30.841947image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:34.557851image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:38.166060image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:41.649841image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:45.697831image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:49.317551image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:52.782379image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:56.574563image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:00.152929image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:03.743182image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:07.238752image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:10.871382image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:14.391551image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:18.308667image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:21.754502image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:25.281224image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:28.818268image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:32.420489image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:36.162996image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:39.615533image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:43.521426image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:26.744268image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:31.016846image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:34.713081image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:38.323711image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:41.802593image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:45.850513image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:49.474990image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:52.928537image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:56.740901image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:00.311685image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:03.889773image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:07.396006image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:11.030912image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:14.624081image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:18.471756image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:21.915037image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:25.450574image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:28.992696image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:32.576406image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:36.319581image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:39.776174image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:43.684706image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:26.907584image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:31.181280image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:34.886292image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:38.469674image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:41.953042image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:46.090132image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:49.618463image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:53.222139image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:56.894663image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:00.465520image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:04.045542image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:07.548522image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:11.181644image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:14.777932image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:18.627709image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:22.063652image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:25.614956image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:29.211179image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:32.871243image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:36.468780image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:39.926471image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:43.821408image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:27.056595image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:31.327142image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:35.048331image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:38.607904image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:42.097689image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:46.259290image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:49.759689image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:53.349046image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:57.044788image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:00.613621image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:04.195035image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:07.697148image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:11.324912image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:14.948939image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:18.776056image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:22.188934image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:25.754442image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:29.348820image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:33.015631image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:36.613573image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:40.075509image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:43.989897image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:27.361789image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:31.496809image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:35.208273image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:38.762923image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:42.274467image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:46.425018image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:49.919393image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:53.515677image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:57.216274image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:00.781106image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:04.383036image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:07.859215image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:11.486359image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:15.153815image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:18.942071image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:22.344157image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:25.913243image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:29.516387image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:33.185173image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:36.780849image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:40.251756image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:44.162183image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:27.653877image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:31.681468image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:35.351996image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:38.922785image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:42.424086image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:46.582921image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:50.092539image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:53.653146image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:57.380693image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:01.080301image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:04.531993image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:08.016416image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:11.639323image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:15.302840image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:19.094321image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:22.481661image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:26.079764image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:29.670213image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:33.332979image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:36.932814image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:40.397016image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:44.318255image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:27.845118image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:31.863496image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:35.502168image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:39.064821image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:42.576382image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:46.733007image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:50.243493image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:53.803299image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:57.535465image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:01.226429image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:04.681718image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:08.178206image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:11.785376image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:15.458147image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:19.250600image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:22.637228image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:26.237090image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:29.817481image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:33.535968image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:37.093090image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:40.684451image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:44.481233image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:28.026949image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:32.039432image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:35.655981image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:39.216610image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:42.783580image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:46.890953image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:50.398107image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:53.957828image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:57.709919image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:01.383371image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:04.837054image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:08.486326image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:11.944879image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:15.615930image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:19.410340image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:22.785723image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:26.391934image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:29.979147image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:33.716461image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:37.249358image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:40.844986image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:44.639178image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:28.198942image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:32.198071image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:35.815511image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:39.382485image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:42.951700image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:47.052314image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:50.552942image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:54.116735image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:13:57.879228image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:01.536799image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:04.998910image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:08.651078image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:12.106418image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:15.767385image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:19.574288image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:22.933514image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:26.547827image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:30.133814image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:33.898422image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:37.401875image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T15:14:40.992037image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Correlations

2023-06-08T15:15:03.254355image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
h_booking_idbooking_datetimecheckin_datecheckout_datehotel_idhotel_country_codehotel_star_ratingaccommadation_type_nameh_customer_idno_of_adultsno_of_childrenno_of_roomlanguageoriginal_selling_amounthotel_area_codehotel_brand_codehotel_chain_codehotel_city_codedistance_booking_checkinamount_guestsamount_nightsprice_per_guest_per_nightcharge_optionguest_is_not_the_customerno_of_extra_bedoriginal_payment_methodoriginal_payment_typeoriginal_payment_currencyis_user_logged_inis_first_bookingrequest_nonesmokerequest_latecheckinrequest_highfloorrequest_largebedrequest_twinbedsrequest_airportrequest_earlycheckindid_cancelcostumer_guest_same_nationpay_now
h_booking_id1.0000.000-0.007-0.007-0.003-0.0010.0040.0050.0040.0030.0010.0070.0090.006-0.000-0.013-0.0100.002-0.0030.0030.0030.0050.0000.0080.0030.0040.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.000
booking_datetime0.0001.0000.6480.6370.0490.067-0.067-0.0530.010-0.088-0.105-0.070-0.092-0.3180.0210.0450.002-0.017-0.618-0.121-0.242-0.2020.2140.0480.0200.0480.0280.0850.1130.0260.2790.0340.1040.1680.0490.0790.0250.2650.0660.282
checkin_date-0.0070.6481.0000.9980.037-0.009-0.014-0.0130.003-0.034-0.044-0.029-0.031-0.0760.0080.003-0.0120.003-0.010-0.048-0.070-0.0290.0250.0140.0080.0200.0130.0560.0110.0120.1810.0110.0300.0970.0290.0300.0000.0210.0160.026
checkout_date-0.0070.6370.9981.0000.036-0.014-0.013-0.0130.003-0.035-0.043-0.028-0.035-0.0440.0070.001-0.0130.0040.006-0.049-0.019-0.0230.0180.0160.0110.0230.0160.0550.0150.0060.1790.0090.0230.0920.0270.0270.0000.0220.0090.023
hotel_id-0.0030.0490.0370.0361.0000.071-0.171-0.154-0.010-0.022-0.048-0.0310.032-0.1100.006-0.0360.0530.026-0.034-0.038-0.021-0.1180.1150.0410.0280.0660.0620.0670.0580.0220.0350.0190.0360.0250.0240.0000.0170.1170.0350.163
hotel_country_code-0.0010.067-0.009-0.0140.0711.000-0.0770.027-0.0090.1050.0030.0040.132-0.1760.0130.006-0.0160.067-0.1040.095-0.074-0.2430.1500.1070.0230.1440.1030.4630.0750.0960.0640.0750.0560.1240.0320.0220.0520.2000.1440.212
hotel_star_rating0.004-0.067-0.014-0.013-0.171-0.0771.0000.3280.0170.0940.0780.0360.0080.445-0.002-0.017-0.097-0.0240.1080.1150.0600.5090.0680.0480.0210.1000.0550.1080.0290.0380.0390.0210.1620.0970.0780.0320.0500.0510.0370.096
accommadation_type_name0.005-0.053-0.013-0.013-0.1540.0270.3281.0000.0030.1520.0630.030-0.0130.222-0.0170.011-0.091-0.0010.0690.1580.0160.2150.0320.0450.0280.0550.0220.0860.0220.0400.0450.0160.0880.0510.0520.0350.0270.0620.0330.046
h_customer_id0.0040.0100.0030.003-0.010-0.0090.0170.0031.0000.0180.0100.006-0.0040.015-0.0020.009-0.025-0.009-0.0050.0210.0050.0080.0200.0230.0000.0300.0630.0570.0320.0150.0170.0430.0070.0290.0240.0000.0000.0180.0140.030
no_of_adults0.003-0.088-0.034-0.035-0.0220.1050.0940.1520.0181.0000.0470.5290.0160.2580.012-0.025-0.0040.0130.1200.9030.010-0.1000.0420.0320.1060.0000.0190.0360.0180.0080.0290.0000.0070.0990.0800.0330.0000.0450.0080.059
no_of_children0.001-0.105-0.044-0.043-0.0480.0030.0780.0630.0100.0471.0000.0640.0580.161-0.018-0.036-0.0140.0170.1240.4320.049-0.0150.0640.0200.0370.0250.0070.0440.0060.0380.0000.0110.0100.0670.0250.0200.0310.0430.0140.091
no_of_room0.007-0.070-0.029-0.028-0.0310.0040.0360.0300.0060.5290.0641.0000.0150.219-0.0050.005-0.001-0.0110.0830.4990.0340.0080.0340.0440.1020.0080.0100.0350.0200.0080.0280.0000.0040.0520.0680.0270.0000.0270.0210.047
language0.009-0.092-0.031-0.0350.0320.1320.008-0.013-0.0040.0160.0580.0151.0000.043-0.034-0.020-0.007-0.0400.1260.036-0.0670.0880.1200.1270.0150.2410.2860.6720.1500.1320.0490.0850.0770.1170.0690.0310.0930.1310.1440.168
original_selling_amount0.006-0.318-0.076-0.044-0.110-0.1760.4450.2220.0150.2580.1610.2190.0431.000-0.032-0.010-0.053-0.0420.4510.2980.6120.7520.0060.0130.0000.0000.0000.0340.0000.0000.0040.0000.0000.0000.0000.0220.0000.0180.0000.013
hotel_area_code-0.0000.0210.0080.0070.0060.013-0.002-0.017-0.0020.012-0.018-0.005-0.034-0.0321.0000.0440.0200.013-0.0190.006-0.000-0.0440.0300.0500.0110.0370.1000.0780.0350.0240.0330.0240.0410.0110.0190.0280.0000.0330.0150.041
hotel_brand_code-0.0130.0450.0030.001-0.0360.006-0.0170.0110.009-0.025-0.0360.005-0.020-0.0100.0441.000-0.050-0.011-0.052-0.037-0.0370.0440.0660.0330.0140.0580.0210.0990.0310.0430.0360.0230.0400.0120.0160.0120.0250.0700.0490.093
hotel_chain_code-0.0100.002-0.012-0.0130.053-0.016-0.097-0.091-0.025-0.004-0.014-0.001-0.007-0.0530.020-0.0501.000-0.013-0.011-0.011-0.022-0.0550.0870.0410.0170.0620.0180.1170.0780.0810.0000.0290.0330.0350.0220.0160.0560.0890.1010.120
hotel_city_code0.002-0.0170.0030.0040.0260.067-0.024-0.001-0.0090.0130.017-0.011-0.040-0.0420.013-0.011-0.0131.0000.0200.0170.009-0.0700.0520.0610.0150.0550.0950.1720.0400.0530.0320.0280.0480.0290.0020.0240.0380.0530.0570.072
distance_booking_checkin-0.003-0.618-0.0100.006-0.034-0.1040.1080.069-0.0050.1200.1240.0830.1260.451-0.019-0.052-0.0110.0201.0000.1580.3360.3040.2100.0440.0080.0410.0260.0810.1220.0310.2010.0290.1060.1310.0280.0690.0350.2780.0610.287
amount_guests0.003-0.121-0.048-0.049-0.0380.0950.1150.1580.0210.9030.4320.4990.0360.2980.006-0.037-0.0110.0170.1581.0000.029-0.0990.0680.0300.1160.0000.0240.0390.0140.0300.0250.0050.0110.1240.0840.0300.0000.0590.0130.096
amount_nights0.003-0.242-0.070-0.019-0.021-0.0740.0600.0160.0050.0100.0490.034-0.0670.612-0.000-0.037-0.0220.0090.3360.0291.0000.1340.0680.0320.0000.0070.0120.0880.0180.0340.0610.0140.0540.0200.0120.0580.0000.1120.0330.095
price_per_guest_per_night0.005-0.202-0.029-0.023-0.118-0.2430.5090.2150.008-0.100-0.0150.0080.0880.752-0.0440.044-0.055-0.0700.304-0.0990.1341.0000.0180.0150.0000.0000.0790.0360.0130.0000.0000.0120.0000.0040.0140.0080.0000.0410.0200.028
charge_option0.0000.2140.0250.0180.1150.1500.0680.0320.0200.0420.0640.0340.1200.0060.0300.0660.0870.0520.2100.0680.0680.0181.0000.0440.0240.1660.0480.1730.0960.0380.0220.0230.0570.0530.0330.0440.0000.3870.0471.000
guest_is_not_the_customer0.0080.0480.0140.0160.0410.1070.0480.0450.0230.0320.0200.0440.1270.0130.0500.0330.0410.0610.0440.0300.0320.0150.0441.0000.0140.0790.0820.1990.0460.1620.0140.0190.0210.0000.0420.0090.0280.0460.0440.044
no_of_extra_bed0.0030.0200.0080.0110.0280.0230.0210.0280.0000.1060.0370.1020.0150.0000.0110.0140.0170.0150.0080.1160.0000.0000.0240.0141.0000.0000.0000.0470.0000.0130.0140.0170.0000.0310.0310.0440.0130.0000.0000.034
original_payment_method0.0040.0480.0200.0230.0660.1440.1000.0550.0300.0000.0250.0080.2410.0000.0370.0580.0620.0550.0410.0000.0070.0000.1660.0790.0001.0000.3410.1630.1230.0960.0480.0330.0930.0940.0550.0000.0540.1480.0960.237
original_payment_type0.0000.0280.0130.0160.0620.1030.0550.0220.0630.0190.0070.0100.2860.0000.1000.0210.0180.0950.0260.0240.0120.0790.0480.0820.0000.3411.0000.3160.1630.0530.0190.0000.0150.0170.0000.0000.0000.0530.0410.068
original_payment_currency0.0090.0850.0560.0550.0670.4630.1080.0860.0570.0360.0440.0350.6720.0340.0780.0990.1170.1720.0810.0390.0880.0360.1730.1990.0470.1630.3161.0000.1990.2180.0740.0810.1920.1500.1040.1250.1050.1770.3940.205
is_user_logged_in0.0000.1130.0110.0150.0580.0750.0290.0220.0320.0180.0060.0200.1500.0000.0350.0310.0780.0400.1220.0140.0180.0130.0960.0460.0000.1230.1630.1991.0000.4790.0190.0500.1190.0220.0000.0000.0560.0950.0320.096
is_first_booking0.0000.0260.0120.0060.0220.0960.0380.0400.0150.0080.0380.0080.1320.0000.0240.0430.0810.0530.0310.0300.0340.0000.0380.1620.0130.0960.0530.2180.4791.0000.0320.0430.0860.0000.0000.0250.0460.0120.1790.038
request_nonesmoke0.0000.2790.1810.1790.0350.0640.0390.0450.0170.0290.0000.0280.0490.0040.0330.0360.0000.0320.2010.0250.0610.0000.0220.0140.0140.0480.0190.0740.0190.0321.0000.0430.0380.0910.0660.0140.0380.0100.0200.021
request_latecheckin0.0000.0340.0110.0090.0190.0750.0210.0160.0430.0000.0110.0000.0850.0000.0240.0230.0290.0280.0290.0050.0140.0120.0230.0190.0170.0330.0000.0810.0500.0430.0431.0000.1120.0390.0040.0110.0290.0140.0110.023
request_highfloor0.0000.1040.0300.0230.0360.0560.1620.0880.0070.0070.0100.0040.0770.0000.0410.0400.0330.0480.1060.0110.0540.0000.0570.0210.0000.0930.0150.1920.1190.0860.0380.1121.0000.1520.0450.0300.1710.0430.0190.054
request_largebed0.0000.1680.0970.0920.0250.1240.0970.0510.0290.0990.0670.0520.1170.0000.0110.0120.0350.0290.1310.1240.0200.0040.0530.0000.0310.0940.0170.1500.0220.0000.0910.0390.1521.0000.3480.0160.0380.0750.0000.052
request_twinbeds0.0000.0490.0290.0270.0240.0320.0780.0520.0240.0800.0250.0680.0690.0000.0190.0160.0220.0020.0280.0840.0120.0140.0330.0420.0310.0550.0000.1040.0000.0000.0660.0040.0450.3481.0000.0000.0020.0150.0050.033
request_airport0.0000.0790.0300.0270.0000.0220.0320.0350.0000.0330.0200.0270.0310.0220.0280.0120.0160.0240.0690.0300.0580.0080.0440.0090.0440.0000.0000.1250.0000.0250.0140.0110.0300.0160.0001.0000.0140.0000.0120.022
request_earlycheckin0.0000.0250.0000.0000.0170.0520.0500.0270.0000.0000.0310.0000.0930.0000.0000.0250.0560.0380.0350.0000.0000.0000.0000.0280.0130.0540.0000.1050.0560.0460.0380.0290.1710.0380.0020.0141.0000.0000.0000.000
did_cancel0.0040.2650.0210.0220.1170.2000.0510.0620.0180.0450.0430.0270.1310.0180.0330.0700.0890.0530.2780.0590.1120.0410.3870.0460.0000.1480.0530.1770.0950.0120.0100.0140.0430.0750.0150.0000.0001.0000.0180.387
costumer_guest_same_nation0.0000.0660.0160.0090.0350.1440.0370.0330.0140.0080.0140.0210.1440.0000.0150.0490.1010.0570.0610.0130.0330.0200.0470.0440.0000.0960.0410.3940.0320.1790.0200.0110.0190.0000.0050.0120.0000.0181.0000.047
pay_now0.0000.2820.0260.0230.1630.2120.0960.0460.0300.0590.0910.0470.1680.0130.0410.0930.1200.0720.2870.0960.0950.0281.0000.0440.0340.2370.0680.2050.0960.0380.0210.0230.0540.0520.0330.0220.0000.3870.0471.000

Missing values

2023-06-08T15:14:44.984145image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-08T15:14:46.009076image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-08T15:14:46.766281image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

h_booking_idbooking_datetimecheckin_datecheckout_datehotel_idhotel_country_codehotel_live_datehotel_star_ratingaccommadation_type_namecharge_optionh_customer_idcustomer_nationalityguest_is_not_the_customerguest_nationality_country_nameno_of_adultsno_of_childrenno_of_extra_bedno_of_roomorigin_country_codelanguageoriginal_selling_amountoriginal_payment_methodoriginal_payment_typeoriginal_payment_currencyis_user_logged_incancellation_policy_codeis_first_bookingrequest_nonesmokerequest_latecheckinrequest_highfloorrequest_largebedrequest_twinbedsrequest_airportrequest_earlycheckinhotel_area_codehotel_brand_codehotel_chain_codehotel_city_codecancellation_datetimedid_canceldistance_booking_checkinamount_guestsamount_nightsprice_per_guest_per_nightcostumer_guest_same_nationpay_now
078614452589186089622942122138883852012-10-04 10:03:004.016Pay Now5310397980746630019China0China12002CN37436.52AlipayCredit CardCNYTrue1D1N_1NFalse1.00.00.00.00.00.00.0583250.0675.0461NaTFalse28212136.376667TrueTrue
1-317592511061617091922222222351920572012-10-01 10:03:003.010Pay Now3627650004083420090Japan0Japan1001JP2654.01American ExpressCredit CardJPYTrue3D1N_1NFalseNaNNaNNaNNaNNaNNaNNaN643296.0191.02249NaTFalse01154.010000TrueTrue
2-5166200042028380517257257258901891142014-06-12 08:05:004.010Pay Now6891579066921110017Taiwan0Taiwan2001TW4399.18VisaCredit CardTWDTrue1D1N_1NFalseNaNNaNNaNNaNNaNNaNNaN2900NaNNaN892NaTFalse02149.590000TrueTrue
361652112785008495662562562572363891132010-08-31 07:16:000.05Pay Now5915665216707180023Turkey0Turkey1001TR4619.36MasterCardCredit CardTRYFalse365D100P_100PFalse0.00.00.01.00.00.00.03110NaNNaN744NaTFalse01119.360000TrueTrue
4-185309213142097356732243245187085572010-07-01 07:38:003.510Pay Later8271660960620410074South Korea0South Korea1001KR27175.52VisaCredit CardKRWTrue3D100P_100PFalse0.00.00.00.00.00.00.03760453.0359.022602018-02-02True2101287.760000TrueFalse
584789739241734153541721961987884721092015-01-15 15:54:003.010Pay Now1760275406207060063South Korea0South Korea2001KR2768.38UnionPay - CreditcardCredit CardKRWFalse365D100P_100PFalseNaNNaNNaNNaNNaNNaNNaN1709NaNNaN1636NaTFalse232217.095000TrueTrue
6-185343779936816258020823123316344641092016-12-19 12:24:003.510Pay Later4700808392292780093South Korea0South Korea1001KR8163.60VisaCredit CardKRWTrue1D1N_100PFalse1.00.00.00.00.00.01.02964442.0600.024772018-08-17True221281.800000TrueFalse
725935972871795715020522222530023331092017-11-11 20:57:002.010Pay Later3622815626917240092Thailand0Thailand2001TH4548.54VisaCredit CardTHBFalse1D1N_1NFalse1.00.00.01.00.00.00.02964NaNNaN2477NaTFalse16238.090000TrueFalse
8-16245881242607000232372582601160850612016-01-27 12:39:003.010Pay Now3580464825046080007South Korea0South Korea3001KR27119.11UnionPay - CreditcardCredit CardKRWFalse7D100P_100PTrue1.00.00.00.01.00.00.03901NaNNaN24592018-08-25True203219.851667TrueTrue
98703485699898329335113230231304893382010-11-17 07:09:003.510Pay Now2129668180375770076China0China2001CN3787.78UNKNOWNInvoiceCNYFalse7D50P_2D100P_100PTrueNaNNaNNaNNaNNaNNaNNaN4806NaNNaN2745NaTFalse1162143.890000TrueTrue
h_booking_idbooking_datetimecheckin_datecheckout_datehotel_idhotel_country_codehotel_live_datehotel_star_ratingaccommadation_type_namecharge_optionh_customer_idcustomer_nationalityguest_is_not_the_customerguest_nationality_country_nameno_of_adultsno_of_childrenno_of_extra_bedno_of_roomorigin_country_codelanguageoriginal_selling_amountoriginal_payment_methodoriginal_payment_typeoriginal_payment_currencyis_user_logged_incancellation_policy_codeis_first_bookingrequest_nonesmokerequest_latecheckinrequest_highfloorrequest_largebedrequest_twinbedsrequest_airportrequest_earlycheckinhotel_area_codehotel_brand_codehotel_chain_codehotel_city_codecancellation_datetimedid_canceldistance_booking_checkinamount_guestsamount_nightsprice_per_guest_per_nightcostumer_guest_same_nationpay_now
46917-3955370707847627756147186189289317452011-10-26 16:50:001.09Pay Now2223633554451910061China0China2001CN37116.77WeChatCredit CardCNYTrue365D100P_100PFalseNaNNaNNaNNaNNaNNaNNaN5262NaNNaN142NaTFalse382319.461667TrueTrue
46918-728025400255349400680270271196931572011-06-29 10:05:004.010Pay Later3057302628167480032South Korea0South Korea1001KR27196.43MasterCardCredit CardKRWTrue3D1N_1NFalseNaNNaNNaNNaNNaNNaNNaN272287.0103.013352018-03-30True18911196.430000TrueFalse
46919803886184919405196732243245812762572015-03-11 14:43:003.010Pay Later8271660960620410074South Korea0South Korea1001KR27132.78VisaCredit CardKRWFalse28D100P_100PTrue0.00.00.00.00.00.00.03760NaNNaN22602018-02-02True2101266.390000TrueFalse
46920-5687848489097746853180215218868632572015-02-26 16:35:001.09Pay Now989627699560000000South Korea0South Korea1001KR2787.01VisaCredit CardKRWTrue2D50P_100PFalseNaNNaNNaNNaNNaNNaNNaN3950NaNNaN2202018-07-03True341329.003333TrueTrue
4692148051481419012198621732452473132844612017-11-06 16:47:003.010Pay Now4543871625495590025Taiwan0Taiwan3001TW43183.54VisaCredit CardTWDTrue3D100PFalse0.00.00.00.00.00.00.05242NaNNaN731NaTFalse713230.590000TrueTrue
4692278593615562332001642582592602504240572017-07-08 14:23:003.018Pay Now2856938804428580064Japan0Japan2001JP26142.68UNKNOWNCredit CardJPYFalse2D100PTrue1.00.00.01.00.00.00.0185NaNNaN2039NaTFalse02171.340000TrueTrue
4692327789414686847586002652702722613173572017-08-15 07:07:002.05Pay Now8706898603750380065South Korea0South Korea1001KR2749.74MasterCardCredit CardKRWTrue1D50P_100PFalseNaNNaNNaNNaNNaNNaNNaN4463NaNNaN25672018-09-22True41224.870000TrueTrue
46924-895279430365271816195203205276008612014-06-19 08:05:003.510Pay Later2011401455184750047Taiwan0Taiwan3001TW43140.12VisaCredit CardTWDTrue7D50P_2D100P_100PFalseNaNNaNNaNNaNNaNNaNNaN1240188.0454.07312018-04-05True1073223.353333TrueFalse
46925-428630490131607762921221321515732951142016-10-04 13:32:003.010Pay Now7713317830746380044Taiwan1Taiwan2001TW4387.30MasterCardCredit CardTWDTrue2D100PFalse1.00.00.00.00.00.00.05208NaNNaN2004NaTFalse02221.825000TrueTrue
46926851601447133080462520724124291737522009-06-28 02:02:003.510Pay Later2430631550982440067Bangladesh1Bangladesh2001BD858.01MasterCardCredit CardUSDTrue2D1N_1NFalse0.00.00.01.00.00.00.02597NaNNaN2302NaTFalse332129.005000TrueFalse